Recent Advances in Genome Editing and Bioinformatics: Addressing Challenges in Genome Editing Implementation and Genome Sequencing

Abstract

Genome-editing technology has advanced significantly since the 2020 Nobel Prize in Chemistry was awarded for the development of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (Cas9). While CRISPR-Cas9 has become widely used in academic research, its social implementation has lagged due to unresolved patent disputes and slower progress in gene function analysis. To address this, new approaches bypassing direct gene function analysis are needed, with bioinformatics and next-generation sequencing (NGS) playing crucial roles. NGS is essential for sequencing the genome of target species, but challenges such as data quality, genome heterogeneity, ploidy, and small individual sizes persist. Despite these issues, advancements in sequencing technologies, like PacBio high-fidelity (HiFi) long reads and high-throughput chromosome conformation capture (Hi-C), have improved genome sequencing. Bioinformatics contributes to genome editing through off-target prediction and target gene selection, both of which require accurate genome sequence information. In this review, I will give updates on the development of genome editing and bioinformatics technologies with a focus on the rapid progress in genome sequencing.

Keywords: bibliome; bioinformatics; database; genome editing; genome sequencing; meta-analysis; next-generation sequencers; pathway; transcriptome.

Figure 1

Strategies for genome editing, including genome sequencing. The gray background is the part with emphasis on bioinformatics.

Figure 2

Next-generation sequencers in use in 2025. The horizontal axis shows the quality value, and the vertical axis shows the number of bases per run.

Figure 3

Expanding scope of bioinformatics.

Figure 4

Fanflow: Functional annotation workflow. (A) Overall workflow in Fanflow. (B) Practical results for silkworm (Bombyx mori) by Fanflow. Fanflow uses sequence analysis and expression information to functionally annotate not only all protein sequences translated from cDNA sequences obtained by transcriptome analysis, but also all non-coding sequences obtained.

Figure 5

Quest for Pathways with eXpression (QPX). From the functional annotation of newly sequenced organism and pathway data from WikiPathways and PMC, QPX can generate customized pathway diagram data in Graphical Pathway Markup Language (GPML) and visualize gene expression data on pathways using the Jupyter notebook.